Conventional versus automated measurement of blood pressure in the office (CAMBO) trial
Bibliographic record
Abstract
BACKGROUND: Effective strategies to identify office-induced hypertension in routine clinical practice are required to improve diagnosis and management of hypertension. OBJECTIVE: To compare the quality and accuracy of automated office blood pressure (AOBP) measurement using the BpTRU device with manual office blood pressure (MOBP) in routine clinical practice using awake ambulatory blood pressure (AABP) as the gold standard. METHODS: Primary care practices in Eastern Canada were allocated by cluster randomization to use of AOBP (36 practices, 52 physicians) or to MOBP (31 practices, 36 physicians) in patients with systolic hypertension. The last routine MOBP reading pre-enrolment was compared to the blood pressure (BP) at the first visit after enrollment and after 2 years of follow-up. The primary outcome measure was the mean difference between the AABP and MOBP versus AOBP. RESULTS: The mean (95% confidence interval) decrease in systolic BP from pre- to post-enrollment was greater (P < 0.001) at the first visit in the 252 AOBP patients [-14.3 (-16.6, -12.0)] compared to the 209 MOBP patients [-8.0 (-2.2, -5.8)]. At Year 2, AOBP decreased by -16.3 (-18.6, -14.1) compared to a decrease in MOBP of -12.4 (-14.7, -10.1) (P = 0.02). The mean difference between systolic AABP and MOBP at the first post-enrollment office visit [-7.3 (-9.7, -4.9)] was greater (P < 0.001) than the difference for AOBP [-1.8 (-4.0, 0.4)]. At Year 2, these differences were -5.2 (-7.5, -3.0) for MOBP and -2.8 (-4.9, -0.7) for AOBP (P = 0.13). CONCLUSIONS: AOBP virtually eliminated office-induced hypertension. The decrease in MOBP was attributed to participation in a research study and not to any specific intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".